Mobility Profiling Using Markov Chains for Tree-Based Object Tracking in Wireless Sensor Networks

摘要

Object tracking in wireless sensor networks is to track moving objects by scattered sensors. These sensors are typically organized into a tree to deliver report messages upon detecting object’s move. Existing tree construction algorithms all require a mobility profile that is obtained based on historical statistics. In this paper, we propose an analytic estimate of such mobility profile based on Markov-chain model. This estimate replaces otherwise experimental process that collects statistical data. Simulation results confirm the effectiveness of the proposed approach.